Enhancing the safety of autonomous vehicles is crucial, especially given recent accidents involving automated systems. As passengers in these vehicles, humans' sensory perception and decision-making can be integrated with autonomous systems to improve safety. This study explores neural mechanisms in passenger-vehicle interactions, leading to the development of a Passenger Cognitive Model (PCM) and the Passenger EEG Decoding Strategy (PEDS). Central to PEDS is a novel Convolutional Recurrent Neural Network (CRNN) that captures spatial and temporal EEG data patterns. The CRNN, combined with stacking algorithms, achieves an accuracy of $85.0\% \pm 3.18\%$. Our findings highlight the predictive power of pre-event EEG data, enhancing the detection of hazardous scenarios and offering a network-driven framework for safer autonomous vehicles.

翻译：提升自动驾驶车辆的安全性至关重要，尤其是在近期涉及自动化系统的事故频发的背景下。作为此类车辆的乘客，人类的感官感知与决策能力可与自动驾驶系统相结合，以提升安全性。本研究探讨了乘客与车辆交互中的神经机制，进而开发了乘客认知模型（PCM）与乘客脑电解码策略（PEDS）。PEDS的核心是一种新颖的卷积循环神经网络（CRNN），用于捕捉脑电数据的时空模式。该CRNN结合堆叠算法，实现了 $85.0\% \pm 3.18\%$ 的准确率。我们的研究结果突显了事件前脑电数据的预测能力，可增强危险场景的检测，并为更安全的自动驾驶车辆提供了一个基于网络的框架。